Discover our IT/OT solutions for industry

We combine industrial automation, Digital Twin models, data analysis and secure communication to support day-to-day plant operations. Our solutions help teams make better decisions, optimize processes and securely exchange data between OT and IT systems.

TwinPilot

TwinPilot is a machine learning model available remotely 24/7 that supports decision-making and control of machines, installations and industrial processes. We begin by creating a Digital Twin of the system, representing its behavior and the relationships between variables. We then use it to train an ML model, including through reinforcement learning (RL), to prepare it to make decisions under different operating conditions. After deployment, the model keeps learning: it uses new data and experience to improve how it responds to changes in the process. It can consider hundreds of factors simultaneously and update decisions as often as every minute, adjusting setpoints to the current situation, production goals and technical constraints. Remote operation provides round-the-clock access to the model and allows it to evolve as the plant’s needs change. TwinPilot works with existing PLCs, which handle basic control and safety functions, while the model’s decisions are implemented within agreed operating limits.

Applications

  • Real-time control of nonlinear systems with multiple interdependent variables.
  • Optimizing electricity consumption in cooling, HVAC, compressors and industrial processes, as well as energy use in boiler plants.
  • Adjusting setpoints to ambient temperature, weather conditions, machine loads and current production demand.
  • Recognizing recurring production cycles and operating patterns to anticipate demand and adjust the installation’s operation in advance.
  • Coordinating interdependent equipment while taking process quality, throughput and technical constraints into account.
  • Optimizing a single machine or a larger installation after integration with its control system.

TwinLogic

TwinLogic translates the results obtained from a trained machine learning model into a mathematical formula or algorithm for deterministic decision-making. We first create a Digital Twin of the system, representing its behavior, the relationships between variables and relevant operating conditions. We use it to train an ML model and then study how the model responds to different inputs, setpoints and scenarios. The findings are used to develop a mathematical formula or algorithm that can run in a PLC or a supervisory system. For the same inputs and the same state, the algorithm produces the same result, making its behavior easier to predict, test and document. The resulting logic operates without continuous model training, within its validated scope of application. If the technology or operating conditions change, we can repeat the research using the Digital Twin and prepare an updated version of the algorithm.

Applications

  • Identifying relationships in installations where simple rules are insufficient for selecting setpoints.
  • Developing a mathematical formula or algorithm for deterministic calculation of control parameters in a PLC or supervisory system.
  • Analyzing historical operation of cooling systems, HVAC, compressors and boiler plants to identify more effective setpoint ranges.
  • Comparing load and environmental scenarios within the model’s validated operating range.
  • Developing characteristic curves, corrections and relationships to support existing controllers.
  • Periodically retraining and validating the model after changes to technology, equipment or operating practices.

TwinScope

TwinScope is designed to study the behavior of machines, installations and processes in a virtual environment. The starting point is a Digital Twin of the system, which we use to train a machine learning model. Research using the trained model makes it possible to simulate different scenarios, examine the influence of multiple factors and compare operating alternatives. This allows us to analyze situations that would be costly, time-consuming, difficult to organize or risky to reproduce on a real installation. We draw insights from the simulations and prepare reports that help evaluate planned changes and guide further work. TwinScope supports verification of the effects of new setpoints, algorithm changes or upgrades, as well as recognition of recurring operating profiles. Each report relates its findings to the assumptions made and the scope of the system model, providing a clear basis for engineering decisions.

Applications

  • Simulating “what if” scenarios involving changes in load, setpoints, configuration or external conditions.
  • Studying unusual and boundary operating conditions that would be risky or costly to reproduce on the real installation.
  • Verifying the effects of setpoint changes or upgrades by comparing before-and-after scenarios in a virtual environment.
  • Recognizing recurring production and load profiles and studying system behavior in the corresponding scenarios.
  • Preparing simulation reports, comparisons and findings to support design, test planning and decisions on further work.

PlantAgent

PlantAgent is an AI assistant that supports the analysis of information from across the business. It combines context from machine logs, databases, documentation and reports to help production and maintenance teams find the information they need faster and understand how events unfolded. Users can ask questions in natural language, compare data from different periods and prepare summaries without manually reviewing multiple sources. We tailor the scope of analysis, access to information and presentation of answers to the organization. Findings and suggested actions support specialists, who can check them against source data before making decisions.

Applications

  • Analyzing controller logs, alarms and events to reconstruct the sequence of downtime incidents and identify timing relationships.
  • Finding information in technical documentation, manuals, service reports and intervention histories.
  • Preparing summaries of production changes, recurring faults and events requiring attention.
  • Asking questions about production and operating data in natural language, within the scope of the available sources.
  • Combining TwinScope findings with information on installation operation, maintenance and technological changes.
  • Supporting the preparation of reports and proposed follow-up checks and actions for review by the technical team.

PlantGate

PlantGate is a communication gateway connecting industrial automation (OT) with information technology (IT) systems. It makes selected machine and controller data available in a structured format using industrial communication protocols, allowing analytics applications, reporting systems and production management tools to use it. Integration includes agreeing on data sources, names, units, transmission frequency and access rules. The solution organizes information exchange and makes it easier to add devices or data consumers to the installation. Any transmission of commands to the OT layer requires separate definition of permissions and execution conditions; basic control and safety functions remain in the PLCs.

Applications

  • Transferring data from PLCs and measuring devices to IT systems using industrial communication protocols.
  • Collecting information on energy consumption, temperatures, pressures, machine states and production counters.
  • Integrating machines with databases, reporting systems and production monitoring applications.
  • Standardizing data structures across multiple devices, including signal names, units and timestamps.
  • Supplying data to TwinPilot, TwinLogic, TwinScope and PlantAgent according to the scope of each deployment.
  • Extending communication to additional lines and locations, with defined access rules, connection diagnostics and handling of transmission interruptions.